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Robust compression and detection of epileptiform patterns in ECoG using a real-time spiking neural network hardware framework

Author

Listed:
  • Filippo Costa

    (Universitätsspital Zürich und Universität Zürich
    University of Zurich and ETH Zurich)

  • Eline V. Schaft

    (University Medical Center Utrecht)

  • Geertjan Huiskamp

    (University Medical Center Utrecht)

  • Erik J. Aarnoutse

    (University Medical Center Utrecht)

  • Maryse A. van’t Klooster

    (University Medical Center Utrecht)

  • Niklaus Krayenbühl

    (University Children’s Hospital Zurich and University of Zurich)

  • Georgia Ramantani

    (University Children’s Hospital Zurich and University of Zurich
    Universität Zürich und ETH Zürich)

  • Maeike Zijlmans

    (University Medical Center Utrecht
    Stichting Epilepsie Instellingen Nederland (SEIN))

  • Giacomo Indiveri

    (University of Zurich and ETH Zurich
    Universität Zürich und ETH Zürich)

  • Johannes Sarnthein

    (Universitätsspital Zürich und Universität Zürich
    Universität Zürich und ETH Zürich)

Abstract

Interictal Epileptiform Discharges (IED) and High Frequency Oscillations (HFO) in intraoperative electrocorticography (ECoG) may guide the surgeon by delineating the epileptogenic zone. We designed a modular spiking neural network (SNN) in a mixed-signal neuromorphic device to process the ECoG in real-time. We exploit the variability of the inhomogeneous silicon neurons to achieve efficient sparse and decorrelated temporal signal encoding. We interface the full-custom SNN device to the BCI2000 real-time framework and configure the setup to detect HFO and IED co-occurring with HFO (IED-HFO). We validate the setup on pre-recorded data and obtain HFO rates that are concordant with a previously validated offline algorithm (Spearman’s ρ = 0.75, p = 1e-4), achieving the same postsurgical seizure freedom predictions for all patients. In a remote on-line analysis, intraoperative ECoG recorded in Utrecht was compressed and transferred to Zurich for SNN processing and successful IED-HFO detection in real-time. These results further demonstrate how automated remote real-time detection may enable the use of HFO in clinical practice.

Suggested Citation

  • Filippo Costa & Eline V. Schaft & Geertjan Huiskamp & Erik J. Aarnoutse & Maryse A. van’t Klooster & Niklaus Krayenbühl & Georgia Ramantani & Maeike Zijlmans & Giacomo Indiveri & Johannes Sarnthein, 2024. "Robust compression and detection of epileptiform patterns in ECoG using a real-time spiking neural network hardware framework," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-47495-y
    DOI: 10.1038/s41467-024-47495-y
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    References listed on IDEAS

    as
    1. Brian DePasquale & Christopher J Cueva & Kanaka Rajan & G Sean Escola & L F Abbott, 2018. "full-FORCE: A target-based method for training recurrent networks," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-18, February.
    2. Nicolas Perez-Nieves & Vincent C. H. Leung & Pier Luigi Dragotti & Dan F. M. Goodman, 2021. "Neural heterogeneity promotes robust learning," Nature Communications, Nature, vol. 12(1), pages 1-9, December.
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